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2026 Observability Predictions - Part 5

In APMdigest's 2026 Observability Predictions Series, industry experts — from analysts and consultants to the top vendors — offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 5 covers APM and infrastructure monitoring.

DEBUGGING IN PRODUCTION

Debugging Moves Safely Into Production: In 2026, DevOps teams will rethink something foundational — the belief that all debugging must happen in pre-production. For years, companies have poured huge amounts of money into maintaining pre-prod environments that are supposed to mirror production — even though, as anyone who works in the field knows, they never fully match. With safe, real-time debugging and AI-powered analysis, teams will be able to diagnose issues directly in production without the fear that shaped DevOps culture for a decade. The impact will be dramatic. Pre-prod environments will get smaller, feedback loops will likely get shorter, and organizations will finally stop treating massive pre-production setups as a security blanket. That freed-up investment will move into automation, process modernization, and AI tooling that actually improves delivery. It's a big shift — and most of the industry won't see it coming until the budget lines start to move.
David Jones
VP of NORAM Solution Engineering, Dynatrace

ANALYST REPORT: 2025 Gartner Magic Quadrant™ for Observability Platforms

CLOUD-NATIVE APM

In 2026, application performance management (APM) will be fully cloud-native, focusing on end-to-end transaction monitoring across distributed systems, including microservices and serverless functions. Gartner® forecasts that over 70% of new APM deployments will emphasize business transaction correlation, linking performance metrics to KPIs like revenue or conversion rates. AI will enable auto-instrumentation and automated dependency mapping, thereby reducing manual efforts and accelerating DevOps pipelines. APM will integrate tightly with observability platforms to provide code-level diagnostics alongside infrastructure and user experience data. The value will shift from solely tracking response times to proactively preventing business-impacting degradations.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

REPORT: Overcoming Roadblocks and Mastering Best Practices in Application Performance Monitoring (APM)

APM ONE-CLICK ROOT CAUSE ANALYSIS

In 2026, APM will finally deliver true one-click root-cause analysis powered by large language models. These systems will fuse metrics, traces, logs, and topology context to surface failure patterns that previously required expert intuition. Complex performance issues will be distilled into clear narrative reports, dramatically reducing the time and expertise needed to understand incidents.
Vladimir Mihailenco 
CEO, Uptrace

MIDDLEWARE TOOLING

In 2026, enterprises will realize that APM and middleware teams are no longer operating in parallel lanes: they are interdependent but require various levels of visibility. APM tools are excellent at observing everything broadly, including middleware, but middleware operations need deeper, specialized intelligence. These teams must understand historical configurations, seasonal patterns, and how to actually fix a bottleneck, not just detect one. And as these environments become more distributed, the skills required to run them effectively will increase as well. Roles may blur, but the need for true middleware expertise will only grow. As hybrid and event-driven architectures grow more complex, the gap between what APM can surface and what middleware teams need to resolve will become too large to ignore. The organizations that succeed will be the ones that pair broad APM observability with dedicated middleware tooling so both teams can work together to predict, diagnose, and remediate issues before they ever impact the business. 
Navdeep Sidhu
CEO, MeshIQ

TOP-DOWN OBSERVABILITY

Alert Fatigue Will Drive Top-Down Observability Adoption: The current enterprise monitoring paradigm is unsustainable. Operations teams are confronted with outputs from 15 or more different monitoring tools, requiring dozens of specialists providing constant vigilance across separate dashboards. The result is alert fatigue, where critical signals drown in noise and burnout becomes endemic. The 2026 solution inverts the traditional approach. Instead of bottom-up monitoring that surfaces every system metric for human review, top-down observability focuses on a handful of business-level KPIs, perhaps just 15 high-level indicators that actually describe to organizational performance. Autonomous systems handle all the low-level monitoring, correlating signals and managing routine issues without escalation. Operations teams only receive alerts when these top-tier KPIs face genuine risk. This approach doesn't sacrifice visibility; it delegates the burden of continuous monitoring to systems better equipped to handle it. The cognitive load on human operators drops dramatically, while the organization maintains, even improves, its ability to detect and respond to meaningful problems.
Efrain Ruh
Regional CTO, Digitate

POST-CRASH-FOCUSED WORLD

The most sophisticated app owners will increasingly move into a post-crash-focused world. Many of your favorite apps have managed to get crash rates down to very manageable levels, and more significant improvements will come in understanding how performance improvements can drive better outcomes. Understanding whether customers are actually achieving their goals in applications will become the new norm, rather than just trying to optimize based on a handful of independent metrics.
Fredric Newberg
CTO, Embrace

By 2026, Mobile Application Performance Management (mAPM) will evolve beyond simple speed and crash reporting to integrate deep behavioral analytics that can distinguish genuine user performance issues from automated bot traffic and malicious API interactions occurring on the client side. This shift will elevate the role of performance monitoring from operational hygiene to a core defense against sophisticated app abuse.
Ted Miracco
CEO, Approov

AI-POWERED MOBILE APM

Mobile devices have long been the go-to screen for humanity, surpassing desktops and laptops. The industry has moved from asking, "Do you have an app for it?" to "Can your app do this automatically?" Mobile application performance management in the coming years will leverage AI to comprehensively study both lab (synthetic) and field (real user) metrics to gather data through predictive crash analytics, battery usage monitoring, and network variability management. 5G and 6G adoption will drive real-time edge optimization for mobile commerce apps. AI will also integrate with CI/CD pipelines to ensure that mobile application performance issues are detected and resolved during and before release (no more "oops" moments and social outrage), safeguarding mobile-first business transactions.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring

ITIM MERGES WITH OBSERVABILITY

IT infrastructure monitoring (ITIM) will eventually merge into full-stack observability by 2026, with infrastructure is widening in reach across multi-cloud environments, software-defined networks, containerized apps, and APIs that spread their tentacles across an exploding internet. ITIM will rely on AI for predictive capacity planning and dynamic thresholds, learning along the way. Monitoring will shift from static alerts to AI-driven baselines that will help teams adapt to hybrid cloud environments with a lot of volatility, uncertainty, complexity, and ambiguity amid changing customer needs. Infrastructure data will increasingly be contextualized with evolving application and business metrics to guide investment and scaling decisions aligned to service impact.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

PREDICTIVE AND SELF-ADAPTING INFRASTRUCTURE

Predictive and Self-Adapting Infrastructure Moves to the Forefront: In 2026, ITOM will shift decisively from reactive troubleshooting to predictive, self-adapting infrastructure. Real-time telemetry, anomaly detection, and trend analytics will anticipate capacity risk, performance degradation, and failure scenarios well before they escalate. Automated intervention workflows will drive scaling, throttling, and configuration adjustments without waiting for human triage. Teams that rely on manual monitoring will face rising outage rates and spiraling operational costs as hybrid complexity continues to grow.
Parker Hathcock
Research Director, ServiceOps, Enterprise Management Associates (EMA)

INFRASTRUCTURE MONITORING

Infrastructure Monitoring Will Gain Ground As a Strategic IT Priority: Over the next couple of years, most IT organizations will acknowledge that the traditional service desk isn't enough for modern infrastructure. Every company now has systems in the cloud, CRMs, data centers or complex integrations that require a completely different kind of support than the everyday devices that employees depend on. Handling this complexity is how IT strengthens its role as a strategic business partner, not a cost center. An IT function focused primarily on help desk operations won't have credibility when leadership needs to make critical infrastructure decisions.
Phil Christianson
Chief Product Officer, Xurrent

ITOM AUTOMATION WITH AIOPS

IT operations management (ITOM) will see increased automation, relying on unified AIOps platforms. The focus in 2026 and beyond will be on finding ways to achieve holistic IT resilience and business continuity, extending into IT-OT convergence (IT being the software layer and OT being the operational technology) alongside proactive cybersecurity. AI-driven anomaly detection, Zero Trust enforcement, and orchestration will be mainstream among ITOM teams in the years ahead.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

By 2026, AIOps will have become a foundational block of ITOM, evolving beyond alert noise reduction to fully autonomous decision-making. The enterprise multi-agent AI systems market is expected to grow rapidly, as companies want to deploy ways to better collaborate during incident diagnosis and remediation. Predictive capacity planning and FinOps capabilities will set the tune for cloud cost optimization through auto-tuning workloads. Low-code/no-code AIOps tools will go a long way in democratizing automation creation for non-experts, thereby increasing adoption and operational efficiency across the industry.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

MACOPS

We believe the emergence of true Mac operations or MacOps will rapidly become best practice. This is enabled by the recent inclusion of a complete Declarative Device Management (DDM) interface on Apple devices.  This tooling will enable IT, MDM providers, and others to move from device polling to true declarative states that report health changes to management tools in near real-time.
Chris Chapman
CTO, MacStadium

IT INFRASTRUCTURE MONITORING MERGES WITH FINOPS

IT Infrastructure Monitoring — The FinOps Forcing Function: By 2026, traditional IT infrastructure monitoring will vanish as a standalone category, fully absorbed into Platform Engineering and FinOps, driven by the business imperative: cost. Infrastructure health — especially for Kubernetes, Serverless, and cloud-native resources — will no longer be measured by generic metrics like CPU or memory usage, but by cost-performance efficiency. Observability platforms will offer out-of-the-box, full-stack cost attribution to microservices, tenants, and features, with "FinOps Observability" dashboards replacing raw metrics by delivering actionable, cost-justified guidance for rightsizing, cutting idle spend, and optimizing multi-cloud deployments.
Afrida Mahbub
VP Product Marketing, Checkmk

ITOM COST AND RESOURCE OPTIMIZATION

Cost and Resource Optimization Take Center Stage: With infrastructure scale expanding and budgets under pressure, 2026 will see cost efficiency, resource utilization, and workload placement optimization become core ITOM success metrics. Dynamic autoscaling, energy-aware scheduling, and AI-assisted capacity planning will become standard practice. Organizations that tie infrastructure decisions to real-time cost and performance insights will unlock measurable competitive advantages. Those dependent on static budgets and manual planning will struggle to support modern applications and AI-driven workloads.
Parker Hathcock
Research Director, ServiceOps, Enterprise Management Associates (EMA)

Go to: 2026 Observability Predictions - Part 6, covering OpenTelemetry

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Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

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Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

2026 Observability Predictions - Part 5

In APMdigest's 2026 Observability Predictions Series, industry experts — from analysts and consultants to the top vendors — offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 5 covers APM and infrastructure monitoring.

DEBUGGING IN PRODUCTION

Debugging Moves Safely Into Production: In 2026, DevOps teams will rethink something foundational — the belief that all debugging must happen in pre-production. For years, companies have poured huge amounts of money into maintaining pre-prod environments that are supposed to mirror production — even though, as anyone who works in the field knows, they never fully match. With safe, real-time debugging and AI-powered analysis, teams will be able to diagnose issues directly in production without the fear that shaped DevOps culture for a decade. The impact will be dramatic. Pre-prod environments will get smaller, feedback loops will likely get shorter, and organizations will finally stop treating massive pre-production setups as a security blanket. That freed-up investment will move into automation, process modernization, and AI tooling that actually improves delivery. It's a big shift — and most of the industry won't see it coming until the budget lines start to move.
David Jones
VP of NORAM Solution Engineering, Dynatrace

ANALYST REPORT: 2025 Gartner Magic Quadrant™ for Observability Platforms

CLOUD-NATIVE APM

In 2026, application performance management (APM) will be fully cloud-native, focusing on end-to-end transaction monitoring across distributed systems, including microservices and serverless functions. Gartner® forecasts that over 70% of new APM deployments will emphasize business transaction correlation, linking performance metrics to KPIs like revenue or conversion rates. AI will enable auto-instrumentation and automated dependency mapping, thereby reducing manual efforts and accelerating DevOps pipelines. APM will integrate tightly with observability platforms to provide code-level diagnostics alongside infrastructure and user experience data. The value will shift from solely tracking response times to proactively preventing business-impacting degradations.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

REPORT: Overcoming Roadblocks and Mastering Best Practices in Application Performance Monitoring (APM)

APM ONE-CLICK ROOT CAUSE ANALYSIS

In 2026, APM will finally deliver true one-click root-cause analysis powered by large language models. These systems will fuse metrics, traces, logs, and topology context to surface failure patterns that previously required expert intuition. Complex performance issues will be distilled into clear narrative reports, dramatically reducing the time and expertise needed to understand incidents.
Vladimir Mihailenco 
CEO, Uptrace

MIDDLEWARE TOOLING

In 2026, enterprises will realize that APM and middleware teams are no longer operating in parallel lanes: they are interdependent but require various levels of visibility. APM tools are excellent at observing everything broadly, including middleware, but middleware operations need deeper, specialized intelligence. These teams must understand historical configurations, seasonal patterns, and how to actually fix a bottleneck, not just detect one. And as these environments become more distributed, the skills required to run them effectively will increase as well. Roles may blur, but the need for true middleware expertise will only grow. As hybrid and event-driven architectures grow more complex, the gap between what APM can surface and what middleware teams need to resolve will become too large to ignore. The organizations that succeed will be the ones that pair broad APM observability with dedicated middleware tooling so both teams can work together to predict, diagnose, and remediate issues before they ever impact the business. 
Navdeep Sidhu
CEO, MeshIQ

TOP-DOWN OBSERVABILITY

Alert Fatigue Will Drive Top-Down Observability Adoption: The current enterprise monitoring paradigm is unsustainable. Operations teams are confronted with outputs from 15 or more different monitoring tools, requiring dozens of specialists providing constant vigilance across separate dashboards. The result is alert fatigue, where critical signals drown in noise and burnout becomes endemic. The 2026 solution inverts the traditional approach. Instead of bottom-up monitoring that surfaces every system metric for human review, top-down observability focuses on a handful of business-level KPIs, perhaps just 15 high-level indicators that actually describe to organizational performance. Autonomous systems handle all the low-level monitoring, correlating signals and managing routine issues without escalation. Operations teams only receive alerts when these top-tier KPIs face genuine risk. This approach doesn't sacrifice visibility; it delegates the burden of continuous monitoring to systems better equipped to handle it. The cognitive load on human operators drops dramatically, while the organization maintains, even improves, its ability to detect and respond to meaningful problems.
Efrain Ruh
Regional CTO, Digitate

POST-CRASH-FOCUSED WORLD

The most sophisticated app owners will increasingly move into a post-crash-focused world. Many of your favorite apps have managed to get crash rates down to very manageable levels, and more significant improvements will come in understanding how performance improvements can drive better outcomes. Understanding whether customers are actually achieving their goals in applications will become the new norm, rather than just trying to optimize based on a handful of independent metrics.
Fredric Newberg
CTO, Embrace

By 2026, Mobile Application Performance Management (mAPM) will evolve beyond simple speed and crash reporting to integrate deep behavioral analytics that can distinguish genuine user performance issues from automated bot traffic and malicious API interactions occurring on the client side. This shift will elevate the role of performance monitoring from operational hygiene to a core defense against sophisticated app abuse.
Ted Miracco
CEO, Approov

AI-POWERED MOBILE APM

Mobile devices have long been the go-to screen for humanity, surpassing desktops and laptops. The industry has moved from asking, "Do you have an app for it?" to "Can your app do this automatically?" Mobile application performance management in the coming years will leverage AI to comprehensively study both lab (synthetic) and field (real user) metrics to gather data through predictive crash analytics, battery usage monitoring, and network variability management. 5G and 6G adoption will drive real-time edge optimization for mobile commerce apps. AI will also integrate with CI/CD pipelines to ensure that mobile application performance issues are detected and resolved during and before release (no more "oops" moments and social outrage), safeguarding mobile-first business transactions.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring

ITIM MERGES WITH OBSERVABILITY

IT infrastructure monitoring (ITIM) will eventually merge into full-stack observability by 2026, with infrastructure is widening in reach across multi-cloud environments, software-defined networks, containerized apps, and APIs that spread their tentacles across an exploding internet. ITIM will rely on AI for predictive capacity planning and dynamic thresholds, learning along the way. Monitoring will shift from static alerts to AI-driven baselines that will help teams adapt to hybrid cloud environments with a lot of volatility, uncertainty, complexity, and ambiguity amid changing customer needs. Infrastructure data will increasingly be contextualized with evolving application and business metrics to guide investment and scaling decisions aligned to service impact.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

PREDICTIVE AND SELF-ADAPTING INFRASTRUCTURE

Predictive and Self-Adapting Infrastructure Moves to the Forefront: In 2026, ITOM will shift decisively from reactive troubleshooting to predictive, self-adapting infrastructure. Real-time telemetry, anomaly detection, and trend analytics will anticipate capacity risk, performance degradation, and failure scenarios well before they escalate. Automated intervention workflows will drive scaling, throttling, and configuration adjustments without waiting for human triage. Teams that rely on manual monitoring will face rising outage rates and spiraling operational costs as hybrid complexity continues to grow.
Parker Hathcock
Research Director, ServiceOps, Enterprise Management Associates (EMA)

INFRASTRUCTURE MONITORING

Infrastructure Monitoring Will Gain Ground As a Strategic IT Priority: Over the next couple of years, most IT organizations will acknowledge that the traditional service desk isn't enough for modern infrastructure. Every company now has systems in the cloud, CRMs, data centers or complex integrations that require a completely different kind of support than the everyday devices that employees depend on. Handling this complexity is how IT strengthens its role as a strategic business partner, not a cost center. An IT function focused primarily on help desk operations won't have credibility when leadership needs to make critical infrastructure decisions.
Phil Christianson
Chief Product Officer, Xurrent

ITOM AUTOMATION WITH AIOPS

IT operations management (ITOM) will see increased automation, relying on unified AIOps platforms. The focus in 2026 and beyond will be on finding ways to achieve holistic IT resilience and business continuity, extending into IT-OT convergence (IT being the software layer and OT being the operational technology) alongside proactive cybersecurity. AI-driven anomaly detection, Zero Trust enforcement, and orchestration will be mainstream among ITOM teams in the years ahead.
Gowrisankar Chinnayan
Director of Product Management, ManageEngine

By 2026, AIOps will have become a foundational block of ITOM, evolving beyond alert noise reduction to fully autonomous decision-making. The enterprise multi-agent AI systems market is expected to grow rapidly, as companies want to deploy ways to better collaborate during incident diagnosis and remediation. Predictive capacity planning and FinOps capabilities will set the tune for cloud cost optimization through auto-tuning workloads. Low-code/no-code AIOps tools will go a long way in democratizing automation creation for non-experts, thereby increasing adoption and operational efficiency across the industry.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

MACOPS

We believe the emergence of true Mac operations or MacOps will rapidly become best practice. This is enabled by the recent inclusion of a complete Declarative Device Management (DDM) interface on Apple devices.  This tooling will enable IT, MDM providers, and others to move from device polling to true declarative states that report health changes to management tools in near real-time.
Chris Chapman
CTO, MacStadium

IT INFRASTRUCTURE MONITORING MERGES WITH FINOPS

IT Infrastructure Monitoring — The FinOps Forcing Function: By 2026, traditional IT infrastructure monitoring will vanish as a standalone category, fully absorbed into Platform Engineering and FinOps, driven by the business imperative: cost. Infrastructure health — especially for Kubernetes, Serverless, and cloud-native resources — will no longer be measured by generic metrics like CPU or memory usage, but by cost-performance efficiency. Observability platforms will offer out-of-the-box, full-stack cost attribution to microservices, tenants, and features, with "FinOps Observability" dashboards replacing raw metrics by delivering actionable, cost-justified guidance for rightsizing, cutting idle spend, and optimizing multi-cloud deployments.
Afrida Mahbub
VP Product Marketing, Checkmk

ITOM COST AND RESOURCE OPTIMIZATION

Cost and Resource Optimization Take Center Stage: With infrastructure scale expanding and budgets under pressure, 2026 will see cost efficiency, resource utilization, and workload placement optimization become core ITOM success metrics. Dynamic autoscaling, energy-aware scheduling, and AI-assisted capacity planning will become standard practice. Organizations that tie infrastructure decisions to real-time cost and performance insights will unlock measurable competitive advantages. Those dependent on static budgets and manual planning will struggle to support modern applications and AI-driven workloads.
Parker Hathcock
Research Director, ServiceOps, Enterprise Management Associates (EMA)

Go to: 2026 Observability Predictions - Part 6, covering OpenTelemetry

Related Links

Hot Topics

The Latest

Crisis Communications: When the Outage Hits, Your Communications Can't Be "Investigating"

Outages aren't new. What's new is how quickly they spread across systems, vendors, regions and customer workflows. The moment that performance degrades, expectations escalate fast. In today's always-on environment, an outage isn't just a technical event. It's a trust event ...

OpenTelemetry: A Complete Telemetry Framework that Grows with You

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

The Quiet Power of Milliseconds in High Traffic Systems

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

Are AI "Autonomous Coworkers" Ready for Payroll?

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI Reaches Pivotal Inflection Point: From Automation to Agentic Intelligence

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...